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One‐sided runs‐rules schemes to monitor autocorrelated time series data using a first‐order autoregressive model with skip sampling strategies

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References

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2017Improved Shewhart-Type Charts for Monitoring Times Between Events42 cited
2018Designing of Run Rules t control charts for monitoring changes in the process mean

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40 cited

2016Simultaneous Use of Runs Rules and Auxiliary Information With Exponentially Weighted Moving Average Control Charts35 cited

Cited by

2022Run rules schemes for statistical process monitoring: a literature review22 cited
2020On autoregressive model selection for the exponentially weighted moving average control chart of residuals in monitoring the mean of autocorrelated processes19 cited
2020A combined mixed- s -skip sampling strategy to reduce the effect of autocorrelation on the X scheme with and without measurement errors19 cited
2019Shewhart-type monitoring schemes with supplementary w-of-w runs-rules to monitor the mean of autocorrelated samples18 cited
2020On monitoring the process mean of autocorrelated observations with measurement errors using the w‐of‐w runs‐rules scheme14 cited
2022Proposed nonparametric runs rules Lepage and synthetic Lepage schemes13 cited
2024Enhanced distribution-free Cucconi schemes based on runs: An application to the surveillance of daily average customer service time12 cited
2020One-sided precedence monitoring schemes for unknown shift sizes using generalized 2-of-(h+1) and w-of-w improved runs-rules12 cited
2020The new synthetic and runs-rules schemes to monitor the process mean of autocorrelated observations with measurement errors12 cited
2020On the design of nonparametric runs‐rules schemes using the Markov chain approach9 cited
2021Comparative analysis between FAR and ARL based control charts with runs rules8 cited
2022Statistical design of an adaptive synthetic X¯$\bar{X}$ control chart for autocorrelated processes8 cited
2022Process Capability Cp Assessment for Auto-Correlated Data in the Presence of Measurement Errors8 cited
2019Exponentially weighted moving average chart with a likelihood ratio test for monitoring autocorrelated processes8 cited